IGGtrop_SH and IGGtrop_rH: Two Improved Empirical Tropospheric Delay Models Based on Vertical Reduction Functions

IGGtrop_SH and IGGtrop_rH are two improved tropospheric delay models that are established based on empirical vertical reduction functions and capable of providing zenith tropospheric delay (ZTD) correction for radio space geodetic analysis without meteorological measurements. The vertical dependence of ZTD mean values is represented by an exponential function (four coefficients in the high latitudes and six coefficients in other regions) and the vertical dependence of ZTD seasonal variation amplitudes is represented by a fifth degree polynomial. IGGtrop_SH considers both annual and semiannual variations of ZTD, while IGGtrop_rH only considers the annual variation of ZTD, which is suitable for applications more concerned about time and data storage space. The new models are validated by international Global Navigation Satellite System service tropospheric products at 292 globally distributed tracking stations between January 2010 and December 2013. The average bias values over the four years are around −0.46 cm for both models; the globally averaged root-mean-square error of IGGtrop_SH is about 3.86 cm and the value of IGGtrop_rH is about 3.97 cm. Comparison between IGGtrop_SH and the previous IGGtrop model suggests that the inclusion of semiannual variation leads to an apparent improvement of ZTD correction performance in the Northern Hemisphere, especially for middle latitudes, while no obvious change is seen in the Southern Hemisphere.

[1]  Alan Dodson,et al.  Assessment of EGNOS Tropospheric Correction Model , 1999 .

[2]  Zhizhao Liu,et al.  A Comprehensive Evaluation and Analysis of the Performance of Multiple Tropospheric Models in China Region , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[3]  Paul Collins,et al.  Tropospheric Delay : Prediction for the WAAS User , .

[4]  C. Hackman,et al.  Computation of a High-Precision GPS-Based Troposphere Product by the USNO , 2011 .

[5]  T. Pany,et al.  The european satellite navigation system Galileo , 2008, Wuhan University Journal of Natural Sciences.

[6]  Chris Rizos,et al.  The International GNSS Service in a changing landscape of Global Navigation Satellite Systems , 2009 .

[7]  Torben Schüler,et al.  The TropGrid2 standard tropospheric correction model , 2013, GPS Solutions.

[8]  Wei Li,et al.  A new global zenith tropospheric delay model IGGtrop for GNSS applications , 2012 .

[9]  Z. Jing.  Establishment of a new global model for zenith tropospheric delay based on functional expression for its vertical profile , 2014 .

[10]  Robert Weber,et al.  Development of an improved empirical model for slant delays in the troposphere (GPT2w) , 2015, GPS Solutions.

[11]  Yuei-An Liou,et al.  New versions of the BDS/GNSS zenith tropospheric delay model IGGtrop , 2014, Journal of Geodesy.

[12]  Yao Yi.  A new global zenith tropospheric delay model GZTD , 2013 .

[13]  Richard B. Langley,et al.  Tropospheric Zenith Delay Prediction Accuracy for High-Precision GPS Positioning and Navigation , 1999 .

[14]  Richard B. Langley,et al.  A North America Wide Area Neutral Atmosphere Model for GNSS Applications , 2009 .

[15]  M. Kanamitsu,et al.  NCEP–DOE AMIP-II Reanalysis (R-2) , 2002 .

[16]  Richard B. Langley,et al.  Assessment of Atmospheric Delay Correction Models for the Japanese MSAS , 2001 .

[17]  Shuanggen Jin,et al.  Seasonal variability of GPS‐derived zenith tropospheric delay (1994–2006) and climate implications , 2007 .

[18]  T. Nilsson,et al.  GPT2: Empirical slant delay model for radio space geodetic techniques , 2013, Geophysical research letters.